Purpose: To identify predictive factors for visual field (VF) fluctuation in glaucoma patients. Design: Retrospective cohort study. Participants: A total of 1392 eyes (816 patients) with 6 or more VFs and 3 years or more of follow-up. Methods: For each eye, the VF mean deviation (MD) and the pointwise sensitivities were regressed against time to model the series trend, and the root mean square error (RMSE) was estimated as a measure of variability. Potential predictors were selected with least absolute shrinkage and selection operator regression and included eye laterality, ethnicity, glaucoma type, intraocular pressure (IOP) fluctuation, baseline best corrected-visual acuity, intervening cataract or glaucoma surgery, length of follow-up, frequency of testing, baseline MD, rates of VF progression, and median false positive (FP) and false negative (FN) responses. Main Outcome Measures: Predictors of global and pointwise VF long-term fluctuation. Results: In the global model, left eye (0.063 dB; P = 0.022), Asian descent (0.265 dB; P = 0.006), larger IOP fluctuation (0.051 dB; P < 0.001), intervening cataract surgery (0.090 dB; P = 0.023), longer follow-up (0.130 dB; P < 0.001), worse baseline MD (–0.145 dB; P < 0.001), faster VF decay rate (–0.090 dB; P < 0.001), and higher FP rate (0.145 dB; P < 0.001) and FN rate (0.220 dB; P < 0.001) were predictors of VF fluctuation. In the pointwise model, larger IOP fluctuation (0.039 dB; P = 0.022), longer follow-up (0.340 dB; P < 0.001), higher VF frequency (0.238 dB; P = 0.002), intervening glaucoma surgery (0.190 dB; P = 0.01), worse baseline MD (–0.535 dB; P < 0.001), faster VF decay rate (–0.340 dB; P < 0.001), and higher FP rate (0.255 dB; P < 0.001) and FN rate (0.395 dB; P < 0.001) were associated with increased fluctuation. The multivariable model explained 57% and 28% of the pointwise and global variances, respectively. Conclusions: This study identified novel predictors of VF fluctuation, and explains nearly 60% of the pointwise variance. In the presence of factors predictive of high fluctuation, increased frequency of testing and better analytics will help to identify VF progression more accurately.
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